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Chief Data Scientist – tracking the rise of senior scientists

Niall Wharton our consultant in charge of our Data Science market discusses the growth of the discipline across industries and the rise of the Data Scientist.

07/04/2014

So, I’m guessing a lot of people are sick of discussing what it takes to become a Data Scientist. It was (and probably continues to be) one of the most exciting and in equal measure frustrating titles out on the job market.

However at this point, anyone involved in the field long enough will begin to have shaped their own ideas about who can truly define themselves as a Data Scientist, or indeed what things one would need to learn to have the right to do so.

This in itself is a clear sign of a market maturing – universally accepted definitions are often a great indicator of when an area has established itself as an essential part of our very culture, rather than being just a temporary fad /(marketing tool!).

This maturity has also been reflected in the requests I’m getting this year and the type of profile global businesses, maturing start-ups and even government departments are desperately searching for. Indeed, the rise of the “Chief Data Scientist” has been an interesting one, and must be placed in its proper context over the last few years.

In the time that I’ve been recruiting specifically for this area, there has been a clear pattern in what I have been searching for;

1. Initially, following what everyone has recognised as an incredible amount of early hype surrounding the field, departments realised they wanted to take on a Data Scientist and/or a Data Engineer to demonstrate the value they could supposedly add to an analytics department across the wider business. In effect, proof of concept projects were key, so those with enough relevant experience were taken on as trailblazers for the area.

2. Within my own consistent clients, having seen these trailblazers prove successful, there were a number of requests for “Junior Data Scientists”. Generally, such hires consisted of those straight from academia (whether straight from MSc up to long term postdoc candidates) with the perfect template and clearly analytical mind set to take on a career in this area. Interestingly, when talking to line managers, these hires were preferred at the time for one of two reasons.

Firstly, the demand placed on the initial Data Science hires was proving so great that support was needed; with great success comes invested interested across a business, and more unrealistic workloads/ expectations. Naturally, the juniors were able to learn some of the necessary commercial aspects of the job whilst also contributing to the projects their seniors were drowning under.

Secondly, a number of the companies I have now worked with saw the clear advantages of having a “Data Scientist” / “Big Data Engineer” on board through other business models and success stories. However, even with their own obvious business needs and genuine commercial issues that could be solved with such a hire, many felt the costs involved were just too great. Supply and demand is a pain, but a very real thing in any walk of life. An excellent alternative has been to “grow their own” data scientists –these juniors have been given the time and space to learn their trade and apply their immense knowledge without the weight of a bumper salary on their shoulders (and in turn have reaped great rewards for the departments they have moved into).

3. All of this has led to a point in 2014 (in the European marketplace at least) where entire Data Science departments have been formed in their own right. More senior hires are being made with more of the aforementioned juniors gaining great commercial insights, and in turn top universities are producing Data Science specific courses to match the constant demand for relevant “juniors” (rather than the few who could truly claim to have done so previously).

As a direct result, we have reached a stage where leadership has become a key issue. If those Data Science focused employees are now appreciated across a whole business, rather than just at the occasional demand of an analytics/BI/insight unit, then who is going to take ownership of their work? Whatever we’re going to call it, the Chief Data Scientist/ Lead Data Scientist/ Director of Data Science is a problem that has been created as a direct result of the evolution of one of the fastest growing fields in business.As with any team environment, natural leaders are already establishing themselves because of their stellar work and intimate knowledge of their own workplace and product. Those who were prudent enough to make relatively experienced hires a few years ago have ready-made leaders to promote from within. However, there are many businesses who have a team of Data Scientists that are still continuing their development, and whilst producing excellent insight for top level business leaders, could do with someone else to guide their research and communicate their findings appropriately.

In this way, demand has gone through the roof. I think one of the key questions to answering any skills gap, as had been demonstrated by the ongoing “Data Scientist” discussion, is to define what it truly means to be one.

Through the requirements of the multiple businesses I have dealt with of both multinational and early stage start-up size, I may be in a unique position to guide candidates interested in such a role through most consistent attributes desired;

1. A clear technical understanding of the problems that the department may face. As a field dealing with the most cutting edge and constantly developing technologies/methods on the market, one of the key frustrations I have heard from those operating within the Data Science market is the lack of technical understanding from their senior figures and leaders. Those who have experienced the development of a Data Science department from scratch, and who still enjoy getting involved with the data in front of them on a daily basis, are going to be enormously successful in finding exciting projects to join. This understanding ensures that targets are ambitious enough without being ludicrous, have direction without stifling innovation, and have a clear business goal but also an awareness of what can and can’t be achieved with the personnel involved.

2. An ability to communicate clearly and capably with both ground level technicians and board room executives. This clearly relates back to my first point, but is massively important and cannot be emphasised enough. A Chief Data Scientist should be able to get involved with and have a technical understanding of the entire scope of any given project, but also be able to explain results to those without technical backgrounds so they can see the value to be gained. In the end, this is only going to create more space for the unit to operate in, less hesitation about further growth, and indeed more companywide trust in the commercial insight being provided. Clearly, that’s what any unit should be striving for!

3. A desire to keep learning and help others continue their own development. So you’re hired as a Chief Data Scientist. Should your drive to learn the latest technologies and improve your ability to gain the most insight form the data in front of you diminish? Of course not. As the most cutting edge area of any analytics department within a company, a clear part of your job will be to keep in touch with developments in the space – this is why within this close knit community we see such a staunch commitment to public speaking from key figures at Data Science specific events.

There is a mutual interest in learning, and mutual commitment to share the knowledge/ breakthroughs discovered along the way. Naturally, this will also apply to the departments that a leader is in charge of – some of the most successful “Chiefs” and “Leads” I have seen have lectured, mentored, and led for a period of their academic career due to a desire to bounce ideas off like minded individuals.

I’d recommend anybody who is interested in such a role getting in touch – relevant experience isn’t always commercially focused, either. A great example is Chris Wiggens, now a Chief Data Scientist with the New York Times . Coming from a Biology and applied Mathematics background, Chris has all of the essential skills listed above. In fact, one of the most exciting companies here in the UK, a social gaming company, has recently done the exact same thing with their lead coming from a research background leading a group aimed at predicting specific disease phenotypes.

I’m always on the lookout for relevant candidates of a senior nature so please feel free to keep in touch and if you want to find out a little more. It’s also worth keeping an eye out on European focused news and jobs by joining my group Data Science Collective on LinkedIn – all contributors to the discussion are welcome!

I’ll be interested to hear all of your thoughts about what I’ve said here in the comment boxes below – do you agree? Are there any extra qualities you feel are vital? Do you think this is an essential piece of a functioning Data Science unit, or do you prefer a more flat structure in your workspace?

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